8A Recommended System for Crop Disease Detection and Yield Prediction Using Machine Learning Approach
Department of Computer Science and Engineering, Sanjay Ghodawat University, Kolhapur, Maharashtra, India
Agriculture is the mainstay of a rising economy in India. Traditionally farmers followed ancestral farming patterns and norms. However, a single farmer cannot be expected to take into account all innumerable factors that contribute to crop growth. A single misguided or imprudent decision by the farmer can have undesirable ramifications. With the advancements in various domains, intelligent agricultural system is needed for upliftment of Indian economy. The collaboration of recommender system with machine learning will lead to Intelligent Agriculture System that helps the farmer community in their decision making of farm management and agribusiness activities such as i) Predicting agriculture commodity market price before cultivation, ii) Determining best cultivars to plant iii) Determine optimum cultivation date v) Evaluate demand and supply risk vi) Investment Prioritizing. It also helps farmer to perform the activities like crop management including applications on yield prediction, disease detection, weed detection, crop quality, and growth prediction etc. This chapter describes the case study on “Crop Disease Detection and Yield prediction”. The study includes identification of crop condition, disease detection, prediction about specific crop and ...
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